Review: Dairy Farm Electricity Use, Conservation, and Renewable Production—A Global Perspective
Notice bibliographique
Résumé
Highlights Studies of electricity use were reviewed, representing five continents. Considering all farm types, electricity use averaged 7.7 kWh 100 kg-1 milk and 612 kWh cow-1 y-1. Pasture-based dairy systems used less electricity than barn-based systems (475 vs. 769 kWh cow-1 y-1). By combining several conservation technologies there is potential to reduce electricity demand by one-third. Dairy farms can reach net zero electricity by combining renewable energy production with conservation. Abstract. This review summarizes electricity use on dairy farms, with a focus on how energy is used, energy use indices (EUI), conservation strategies, and generation of renewable energy to reach net zero. EUI of electricity consumption varied between the identified studies primarily based on farm management system (confined, pasture-based), housing type (tie-stall, free-stall), and region (North America, Europe, Asia, Africa, Oceania). The highest electricity usage was associated with milking and milk cooling systems, which, on average, accounted for 23% and 22% of total electricity use, respectively. Energy use scaled per cow (EUIc) was lower, on average, for pasture-based dairy systems than for confined systems (475 vs. 769 kWh cow-1 y-1). Considering milk production, the average EUI scaled to milk (EUIm) was lower for pasture-based systems (6.6 kWh 100 kg-1) than for confined systems 9.2 kWh 100 kg-1. Considering all non-irrigated farm types, EUIm averaged 7.7 kWh 100 kg-1 and EUIc averaged 612 kWh cow-1 y-1. There was a large range of EUI, with higher values associated with automated milking systems and irrigation. Electricity consumption by the global dairy sector (excluding irrigation) was estimated using the average EUIm at approximately 64.2 TWh y-1. The main conservation technologies include variable speed drives (milk vacuum pumps, milking systems, fans), pre-cool heat exchangers, refrigeration heat recovery systems, energy-efficient light fixtures (compact fluorescents, light emitting diodes), and efficient ventilation (high-volume low-speed fans). Theoretical savings of up to 32% overall could be achieved by combining several technologies. Feedback from electricity monitoring can inform dairy farmers of their energy use pattern to guide decisions to reduce consumption. Tools for predicting energy use and related costs on dairy farms, which can indicate potential energy savings from operational changes, were reviewed. By combining conservation methods with renewable energy from biogas or solar, many dairy farms can produce enough electricity to reach net zero electricity. For example, a hypothetical barn-based 250 milking-cow dairy farm consumed 1021 kWh cow-1 y-1, on average, and could produce approximately 1095 kWh cow-1 y-1 using a biodigester or 960 kWh cow-1 y-1 using rooftop photovoltaic solar panels. Keywords: Conservation, Dairy footprint, Electricity use, Electricity partitioning, Energy utilization index, Renewable energy.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,006 | 0,010 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».